CN109655805B - Laser radar positioning method based on scan line segment coincidence length estimation - Google Patents
Laser radar positioning method based on scan line segment coincidence length estimation Download PDFInfo
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Abstract
The invention discloses a laser radar positioning method based on scanning line segment coincidence length estimation, which comprises the steps of firstly carrying out coordinate transformation and spatial filtering on original data scanned by a single line laser radar; then, carrying out scanning data clustering processing and line segment identification to construct a local 2D environment map; then, constructing a complete line segment set, and selecting the longest complete line segment and a known 2D environment map to perform complete line segment matching pair calculation by using the length of the complete line segment as a matching parameter; and finally, estimating the pose of the laser radar, calculating the coincidence length of the corresponding scanning line segments by different matching pairs, and determining the pose. The method carries out the evaluation of the positioning accuracy by calculating the length of the overlapped line segment of the matched complete line segment, solves the problem of inaccurate positioning when the complete line segment is missing, and has the advantages of good adaptability, high positioning accuracy, high robustness and the like.
Description
Technical Field
The invention relates to a laser radar positioning method, in particular to a laser radar positioning method based on scan line segment overlapping length estimation.
Background
Indoor positioning is the most basic link in autonomous navigation of a mobile robot. In the prior art, complete line segments are mostly used for indoor positioning, for example, the thesis "mobile robot obstacle detection based on laser radar and self-positioning [ D ], (college, zhejiang university, 2002) uses laser radar to acquire complete line segments in a local environment map and match the complete line segments with a global map, and the method has the advantages of small calculation amount and good robustness. However, when the matching accuracy is evaluated, the parameters of the complete line segments are selected for evaluation, and the condition that the number of the complete line segments is insufficient is not considered, so that inaccurate positioning is caused. Paper positioning research [ D ] of indoor autonomous mobile robots (in university of yanshan, 2009) establishes a matching hypothesis based on a length relationship of a complete line segment, evaluates the matching hypothesis according to a position relationship of the complete line segment and a feature point to obtain a matching matrix and a sign matrix, and finally proposes an optimal matching search algorithm according to the matching matrix and the sign matrix. The method avoids frequent coordinate transformation, reduces system consumption, and has good real-time performance, but the method does not consider the problems of insufficient complete line segments and feature points under special conditions.
Disclosure of Invention
The invention aims to provide a laser radar positioning method based on the estimation of the overlapping length of a scanning line segment, and the method solves the problem of inaccurate positioning under the condition of complete line segment deletion in the prior art.
The technical solution for realizing the purpose of the invention is as follows: a laser radar positioning method based on scan line segment coincidence length estimation specifically comprises the following steps:
step 2, clustering processing and line segment identification are carried out on the scanning data, and a local 2D environment map is constructed;
and 4, estimating the pose of the laser radar, calculating the coincidence length of the corresponding scanning line segments by different matching pairs, and determining the pose.
Compared with the prior art, the invention has the following remarkable advantages: 1) according to the method, the laser radar point cloud data is subjected to region segmentation by using a fast neighbor clustering algorithm, two adjacent points are not taken for comparison, and the two adjacent points are compared at intervals, so that the calculated amount is greatly reduced, and the timeliness of point cloud data classification is improved; 2) when the line segment is identified, the situation of deficient segmentation is eliminated as much as possible by adopting a smaller threshold, and then whether the included angle between adjacent line segments is greater than 179 degrees is judged after straight line fitting to determine whether the adjacent line segments belong to the same line segment, so that the problems of over-segmentation and under-segmentation are solved; 3) the method carries out the evaluation of the positioning accuracy by calculating the length of the overlapped line segment of the matched complete line segment, solves the problem of inaccurate positioning when the complete line segment is missing, and has the advantages of good adaptability, high positioning accuracy, high robustness and the like.
Drawings
FIG. 1 is a flowchart of a laser radar positioning method based on scan line segment overlap length estimation according to the present invention.
FIG. 2 is a schematic diagram of the coordinate transformation of the present invention.
FIG. 3 is a diagram illustrating the complete line segment determination according to the present invention.
FIG. 4 is a schematic diagram of laser radar pose estimation according to the present invention.
FIG. 5 is a schematic diagram of the length of the overlapping portion of the coincident line segments of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings.
As shown in fig. 1, the laser radar positioning method based on the scan line segment overlap length estimation specifically includes the following steps:
coordinate transformation and spatial filtering: the raw data points acquired by the single line laser radar are the range values and the scan angles corresponding thereto, and thus the data representation thereof is represented in polar coordinates, i.e., (r, α). For the subsequent construction of an environment map, firstly, coordinate conversion is performed on local original data acquired by a laser radar, polar coordinates of the local original data are converted into rectangular coordinates, a local rectangular coordinate system of the laser radar is established as shown in fig. 2, a motion direction of the local rectangular coordinate system is a positive y-axis direction, and the positive x-axis direction is obtained by rotating a y-axis clockwise by 90 degrees. Assuming a certain scanning point of the laser radar as a point P, the polar coordinates (r, theta) of the point P are converted into rectangular coordinates (x)p,yp) As shown in the following formula:
xp＝rcosθ
yp＝rsinθ
spatial filtering: the method comprises the steps of smoothing original data by adopting a spatial mean filtering method, selecting a window with the length of 3, and enabling the traditional spatial mean filtering method to have poor smoothing effect on edge data, so that aiming at the improvement of the edge effect, edge points are determined firstly, the distance value of a certain point is compared with the distance values of two adjacent points on the left and right, and if the absolute value of the difference value between the two points is greater than a set threshold value, the point is the edge point. The non-edge points are then spatially mean filtered.
Step 2, clustering processing and line segment identification are carried out on the scanning data, and a local 2D environment map is constructed;
clustering the scanning data: a fast neighbor clustering method based on a self-adaptive threshold is adopted to segment the region of point cloud data, and the specific method comprises the following steps: taking the first point as an initial boundary point of a first object, taking the first point as a first class, comparing every two points, if the distance between the two points is less than a threshold value 2TD, regarding the point as belonging to the same class as the previous point, and adding the point into the current class; if the distance between two points is greater than the threshold 2TD, the point is considered not to belong to the current class, a new class is started, and the point is taken as the starting point of the new class, but at the moment, whether the distance between the middle point and the previous point is smaller than another threshold TD is also needed to be judged to determine the class of the middle point, if the distance is smaller than the TD, the middle point is classified as the previous class, otherwise, the distance is considered as the class of the next point, and the comparison is repeated. The adaptive threshold TD is calculated as follows:
TD＝αΔr
where Δ r denotes a distance between two adjacent points, Δ r is rsin Δ θ, and Δ θ is an angular resolution of the lidar, which is 0.1667 ° in this embodiment, where α is set to 8 according to a ranging accuracy of the lidar in combination with characteristics of an indoor environment.
Line segment identification: the segment segmentation is carried out by adopting improved IEPF, and the specific process is as follows: connecting the starting point and the end point in the data point set in the same region into a line segment, calculating the distance from each point in the point set to the line segment, finding out the point with the maximum distance, and if the distance is greater than a set threshold value, dividing the point set into two new point sets by taking the point as a boundary point. And repeating the process for the two new point sets until the distances from all the points to the line segments are not greater than a set threshold value, finishing segmentation, and finally performing straight line fitting on the segmented point cloud data by using RANSAC. For under-segmentation, the threshold is set as small as possible to ensure sufficient segmentation and eliminate the situation of under-segmentation as much as possible, wherein the threshold is generally between 0.05 and 0.15. For the over-segmentation situation, after straight line fitting, whether an included angle between adjacent line segments is larger than a certain threshold angle is judged, wherein the threshold angle is generally between 170 ° and 179 ° to determine whether the adjacent line segments belong to the same line segment.
Constructing a local 2D environment map: and representing the indoor local 2D environment map by using the line segment characteristics.
constructing a complete line segment set: the local environment map has complete line segments and incomplete line segments, and the complete line segments need to be determined first, as shown in fig. 3, a point VS,VEThe virtual extension points are respectively the two end points S and E of the line segment, and the point O is the origin of the laser radar. The specific determination conditions are as follows: 1) for a certain line segment of a local map acquired by a laser radar, extending the line segment to two ends of the line segment by a virtual point respectively, and if the distances from the two virtual extending points to an origin (the origin of the laser radar) are smaller than the actual known distances, the line segment must be a complete line segment, OVS＜OS',OVE< OE', therefore SE is a complete line segment; 2) the segments whose two end points are each connected to two different segments must be complete segments, OVS＞OS',OVE> OE', and SE is therefore a partial line segment.
And (3) calculating matching pairs of the complete line segments: and selecting the complete line segment with the longest length as a matching parameter to search the global map for the complete line segment which is possibly matched with the complete line segment. If the difference between the lengths of the two complete line segments is within a threshold range, then the two complete line segments are considered as a pair of possible matching pairs.
And 4, estimating the pose of the laser radar, calculating the coincidence length of the corresponding scanning line segments by different matching pairs, and determining the pose.
And (3) estimating the pose of the laser radar: and respectively estimating the pose of the laser radar at the moment through coordinate transformation according to each pair of possible matching pairs. The specific coordinate transformation formula is as follows: setting the pose of the position of the laser radar at a certain moment in the global rectangular coordinate system as R ═ xr,yr,α]TLocal coordinate (x) of a pointC,yC) And its global coordinate (x)G,yG) Can be represented by the following formula:
there are two cases of correspondence between the end points of two corresponding complete line segments: 1) the starting point and the end point of the complete line segment in the local map respectively correspond to the starting point and the end point of the complete line segment in the global map in sequence; 2) the starting point of the complete line segment in the local map corresponds to the end point of the complete line segment in the global map, and the end point of the complete line segment in the local map corresponds to the starting point of the complete line segment in the global map.
Thus, in the first case, the relation is obtained from the above equation:
in the second case, the relation is obtained according to the above formula:
in order to obtain the position and pose rotation angle alpha of the laser radar, the included angle between the matched line segment L and the x axis of the local coordinate is assumed to be thetaLAnd the angle with the X axis of the global coordinate is thetaGThen, from the geometric relationship in fig. 4, α can be obtained as shown in the following equation:
α＝θG-θL
calculating the overlapping length of the scanning line segments corresponding to different matching pairs: the overlapping lengths of the scan line segments corresponding to different matching pairs are calculated, and then the lengths of the overlapping portions of all the overlapping line segments except the line segments in the matching pairs are superposed, as shown in fig. 5, the line segment AB and the line segment CD are coincident line segments, and the length of the overlapping portions is l. If the length is longer, the accuracy of the match is higher. Here, the determination conditions for the overlapped line segments are: both end points of the line segment of the local map are on the corresponding matching line segment of the global map.
Determining the pose based on the coincidence length: and the matching pair corresponding to the longest coincidence length is the optimal matching pair, and the estimated position posture of the laser radar is the position posture of the current laser radar in the indoor environment map.
Claims (9)
1. A laser radar positioning method based on scan line segment coincidence length estimation is characterized by comprising the following steps:
step 1, carrying out coordinate transformation and spatial filtering on original data scanned by a single-line laser radar;
step 2, clustering processing and line segment identification are carried out on the scanning data, and a local 2D environment map is constructed;
step 3, constructing a complete line segment set, selecting the longest complete line segment and a known 2D environment map to perform complete line segment matching pair calculation by using the length of the complete line segment as a matching parameter;
step 4, estimating the pose of the laser radar, calculating the coincidence length of the corresponding scanning line segments by different matching pairs, and determining the pose;
in step 4, when calculating the overlapping length of the scan line segments corresponding to different matching pairs, firstly determining the overlapping line segments from the different matching pairs, wherein the determination conditions are as follows: and calculating the lengths of the overlapped parts of the overlapped line segments by using the two end points of the line segments of the local map on the corresponding matched line segments of the global map, finally overlapping the lengths of the overlapped parts of all the overlapped line segments except the line segments of the matched pairs, selecting the matched pair corresponding to the longest overlapped length as the optimal matched pair, and determining the estimated laser radar pose as the pose of the current laser radar in the indoor environment map.
2. The lidar positioning method based on estimation of scan line segment coincidence length of claim 1, wherein in step 1, local raw data obtained by the lidar is subjected to coordinate transformation, and polar coordinates of the local raw data are transformed into rectangular coordinates, particularly in lidar transmissionThe center is the origin of coordinates, the moving direction is the positive direction of the y axis, the y axis starts to rotate clockwise by 90 degrees and is the positive direction of the x axis to establish a local rectangular coordinate system of the laser radar, a certain scanning point of the laser radar is set as a point P, and the polar coordinates (r, theta) of the point P are converted into rectangular coordinates (x, theta)p,yp) As shown in the following formula:
xp＝r cosθ
yp＝r sinθ。
3. the lidar positioning method based on scan line segment coincidence length estimation of claim 1, wherein in step 1, the spatial filtering employs an improved spatial mean filtering method to improve the smoothing effect of the edge data, firstly, the edge point is determined, the distance value of a certain point is compared with the distance values of the adjacent left and right points, if the absolute value of the difference between the two points is greater than a set threshold, the point is determined as the edge point, and then the spatial mean filtering is performed on the non-edge points.
4. The laser radar positioning method based on the estimation of the scan line segment overlapping length according to claim 1, wherein in the step 2, the scan data clustering process adopts a fast neighbor clustering method based on an adaptive threshold to perform region segmentation on point cloud data, and the specific method is as follows: taking the first point as an initial boundary point of a first object, taking the first point as a first class, comparing every two points, if the distance between the two points is less than a threshold value 2TD, regarding the point as belonging to the same class as the previous point, and adding the point into the current class; if the distance between two points is greater than the threshold 2TD, the point is considered not to belong to the current class, a new class is started, and the point is taken as the starting point of the new class, but at the moment, whether the distance between the middle point and the previous point is smaller than another threshold TD or not is also required to be judged, if the distance is smaller than the TD, the middle point is classified as the previous class, otherwise, the middle point and the next point are considered as the class, the comparison is repeated, wherein the TD is an adaptive threshold, and the calculation is carried out in the following mode:
TD＝βΔr
where Δ r represents a distance between two adjacent points, Δ r ═ r sin Δ θ, r is a polar coordinate radius obtained by the radar, Δ θ is an angular resolution of the lidar, and β is set to 8 according to a distance measurement accuracy of the lidar in combination with characteristics of an indoor environment.
5. The lidar positioning method based on scan line segment coincidence length estimation according to claim 1, wherein in step 2, the line segment identification uses improved IEPF for segment segmentation, and the specific process is as follows: connecting a starting point and an end point in a data point set in the same region into a line segment, calculating the distance from each point in the point set to the line segment, finding out the point with the largest distance, if the distance is greater than a set threshold value, dividing the original point set into two new point sets by taking the point as a boundary point, repeating the process on the two new point sets until the distances from all the points to the line segment are not greater than the set threshold value, finishing the division, and finally performing straight line fitting on the divided point cloud data by using RANSAC.
6. The lidar positioning method based on scan line segment coincidence length estimation of claim 5, wherein in the line segment identification, the problems of over-segmentation and under-segmentation occur, and for the under-segmentation, the threshold is set to be as small as possible to ensure sufficient segmentation and to exclude the condition of under-segmentation as possible, wherein the threshold is [0.05,0.15], and for the over-segmentation, after straight line fitting, whether the included angle between adjacent line segments is larger than a certain threshold angle is determined to determine whether the adjacent line segments belong to the same line segment, and the threshold angle is [170 °,179 ° ].
7. The lidar positioning method based on scan line segment coincidence length estimation of claim 1, wherein in step 3, when constructing the complete line segment set, the specific determination conditions of the complete line segment are as follows: 1) for a certain line segment of a local map acquired by a laser radar, extending the certain line segment to two ends of the certain line segment by a virtual point, wherein if the distances from the two virtual extending points to the origin of the laser radar are smaller than those of the two virtual extending points which are actually known, the line segment is necessarily a complete line segment; 2) the segments where the two end points are each connected to two different segments must be complete segments.
8. The lidar positioning method based on scan line segment coincidence length estimation of claim 1, wherein in step 3, the complete line segment matching pair calculation uses the length of the complete line segment as the matching parameter, the complete line segment with the longest length is selected to search the global map for the complete line segment that is likely to match, and if the difference between the lengths of the two complete line segments is within a threshold range, the two complete line segments are considered as a pair of possible matching pairs.
9. The lidar positioning method based on estimation of the scan line segment coincidence length of claim 1, wherein in step 4, the lidar pose estimation estimates the pose of the lidar at the moment through coordinate transformation according to each pair of possible matching pairs, and the specific coordinate transformation formula is as follows: setting the pose of the position of the laser radar at a certain moment in the global rectangular coordinate system as R ═ xr,yr,α]TThe local coordinate (x) of a pointC,yC) And its global coordinate (x)G,yG) Is represented by the following formula:
there are two cases of correspondence between the end points of two corresponding complete line segments: 1) the starting point and the end point of the complete line segment in the local map respectively correspond to the starting point and the end point of the complete line segment in the global map in sequence; 2) the starting point of the complete line segment in the local map corresponds to the end point of the complete line segment in the global map, and the end point of the complete line segment in the local map corresponds to the starting point of the complete line segment in the global map;
thus, in the first case, the relation is obtained from the above equation:
in the second case, the relation is obtained according to the above formula:
in order to obtain the position and pose rotation angle alpha of the laser radar, the included angle between the matched line segment L and the x axis of the local coordinate is assumed to be thetaLAnd the angle with the X axis of the global coordinate is thetaGThen, α can be obtained as shown in the following formula:
α＝θG-θL。
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| CN111595347B (en) * | 2020-06-18 | 2022-02-08 | 上海大学 | Method and system for determining position of mobile robot |
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